In your final repo, there should be an R markdown file that organizes all computational steps for evaluating your proposed Facial Expression Recognition framework.
This file is currently a template for running evaluation experiments. You should update it according to your codes but following precisely the same structure.
if(!require("EBImage")){
install.packages("BiocManager")
BiocManager::install("EBImage")
}
if(!require("R.matlab")){
install.packages("R.matlab")
}
if(!require("readxl")){
install.packages("readxl")
}
if(!require("dplyr")){
install.packages("dplyr")
}
if(!require("readxl")){
install.packages("readxl")
}
if(!require("ggplot2")){
install.packages("ggplot2")
}
if(!require("caret")){
install.packages("caret")
}
if(!require("glmnet")){
install.packages("glmnet")
}
if(!require("WeightedROC")){
install.packages("WeightedROC")
}
if(!require("gbm")){
install.packages("gbm")
}
if(!require("xgboost")){
install.packages("xgboost")
}
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if(!require("caret")){
install.packages("caret")
}
# Install Miniconda (https://docs.conda.io/en/latest/miniconda.html)
if(!require("keras")){
install.packages("keras")
}
if(!require("tensorflow")){
install.packages("tensorflow")
install_tensorflow()
}
use_condaenv("r-tensorflow")
library(keras)
library(tensorflow)
library(R.matlab)
library(readxl)
library(dplyr)
library(EBImage)
library(ggplot2)
library(caret)
library(glmnet)
library(WeightedROC)
library(gbm)
require(xgboost)
library(caret)
set.seed(2040)
# setwd("~/Project3-FacialEmotionRecognition/doc")
# here replace it with your own path or manually set it in RStudio to where this rmd file is located.
# use relative path for reproducibility
Provide directories for training images. Training images and Training fiducial points will be in different subfolders.
train_dir <- "../data/train_set/" # This will be modified for different data sets.
train_image_dir <- paste(train_dir, "images/", sep="")
train_pt_dir <- paste(train_dir, "points/", sep="")
train_label_path <- paste(train_dir, "label.csv", sep="")
In this chunk, we have a set of controls for the evaluation experiments.
# run.cv <- TRUE # run cross-validation on the training set
# sample.reweight <- TRUE # run sample reweighting in model training
# K <- 5 # number of CV folds
# run.feature.train <- TRUE # process features for training set
# run.test <- TRUE # run evaluation on an independent test set
# run.feature.test <- TRUE # process features for test set
sample.reweight <- TRUE # run sample reweighting in model training
K <- 5 # number of CV folds
run.feature.train <- TRUE # process features for training set
run.feature.test <- TRUE # process features for test set
run.cv_gbm <- TRUE # run GBM cross-validation on the training set
run.test_gbm <- TRUE # run GBM evaluation on an independent test set
run.cv_xgboost <- TRUE # run DNN cross-validation on the training set
run.test_xgboost <- TRUE # run DNN evaluation on an independent test set
run.cv_dnn <- TRUE # run DNN cross-validation on the training set
run.test_dnn <- TRUE # run DNN evaluation on an independent test set
Using cross-validation or independent test set evaluation, we compare the performance of models with different specifications. In this part, we tune parameter n.trees and shrinkage for GBM.
# GBM parameters
n.trees <- c(500, 100, 1500)
shrinkage <- c(0.01, 0.05, 0.1)
# XGBoost parameters
eta <- c(0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5)
colsample_bytree <- c(0.2, 0.5, 0.6, 0.7, 0.8, 0.9)
#train-test split
info <- read.csv(train_label_path)
n <- nrow(info)
n_train <- round(n*(4/5), 0)
train_idx <- sample(info$Index, n_train, replace = F)
test_idx <- setdiff(info$Index, train_idx)
If you choose to extract features from images, such as using Gabor filter, R memory will exhaust all images are read together. The solution is to repeat reading a smaller batch(e.g 100) and process them.
n_files <- length(list.files(train_image_dir))
image_list <- list()
for(i in 1:100){
image_list[[i]] <- readImage(paste0(train_image_dir, sprintf("%04d", i), ".jpg"))
}
Fiducial points are stored in matlab format. In this step, we read them and store them in a list.
#function to read fiducial points
#input: index
#output: matrix of fiducial points corresponding to the index
readMat.matrix <- function(index){
return(round(readMat(paste0(train_pt_dir, sprintf("%04d", index), ".mat"))[[1]],0))
}
#load fiducial points
fiducial_pt_list <- lapply(1:n_files, readMat.matrix)
strings not representable in native encoding will be translated to UTF-8
save(fiducial_pt_list, file="../output/fiducial_pt_list.RData")
The follow plots show how pairwise distance between fiducial points can work as feature for facial emotion recognition.
Figure1
feature.R should be the wrapper for all your feature engineering functions and options. The function feature( ) should have options that correspond to different scenarios for your project and produces an R object that contains features and responses that are required by all the models you are going to evaluate later.
feature.Rsource("../lib/feature.R")
tm_feature_train <- NA
if(run.feature.train){
tm_feature_train <- system.time(dat_train <- feature(fiducial_pt_list, train_idx))
save(dat_train, file="../output/feature_train.RData")
}else{
load(file="../output/feature_train.RData")
}
tm_feature_test <- NA
if(run.feature.test){
tm_feature_test <- system.time(dat_test <- feature(fiducial_pt_list, test_idx))
save(dat_test, file="../output/feature_test.RData")
}else{
load(file="../output/feature_test.RData")
}
# # PCA of features
# source("../lib/feature_pca.R")
#
# if(TRUE){
# tm_feature_train_pca <- system.time({feature_train_pca = as.matrix(feature_pca(dat_train));
# feature_test_pca = as.matrix(feature_pca(dat_test))
# })
# save(feature_train_pca, file="../output/feature_train_pca.RData")
# save(feature_test_pca, file="../output/feature_test_pca.RData")
# }else{
# load(file="../output/feature_train_pca.RData")
# load(file="../output/feature_test_pca.RData")
# }
Call the train model and test model from library.
train.R and test.R should be wrappers for all your model training steps and your classification/prediction steps.
train.R
test.R
source("../lib/train_gbm.R")
source("../lib/test_gbm.R")
Visualize cross-validation results.
res_cv_gbm <- as.data.frame(res_cv_gbm)
if(run.cv_gbm){
p1 <- res_cv_gbm %>%
ggplot(aes(x = n.trees, y = mean_error,
ymin = mean_error - sd_error, ymax = mean_error + sd_error)) +
geom_crossbar() +
facet_wrap(~shrinkage)+
theme(axis.text.x = element_text(angle = 90, hjust = 1))
p2 <- res_cv_gbm %>%
ggplot(aes(x = n.trees, y = mean_AUC,
ymin = mean_AUC - sd_AUC, ymax = mean_AUC + sd_AUC)) +
geom_crossbar() +
facet_wrap(~shrinkage)+
theme(axis.text.x = element_text(angle = 90, hjust = 1))
print(p1)
print(p2)
}
# par_n.trees_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 1])
# par_shrinkage_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 2])
par_n.trees_best <- 500
par_shrinkage_best <- 0.05
# training weights
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}
tm_train <- NA
if (sample.reweight){
tm_train <- system.time(fit_train <- train_gbm(feature_train, label_train, w = weight_train, par_n.trees_best, par_shrinkage_best))
} else {
tm_train <- system.time(fit_train <- train_gbm(feature_train, label_train, w = NULL, par_n.trees_best, par_shrinkage_best))
}
save(fit_train, file="../output/fit_train_gbm.RData")
tm_test = NA
feature_test <- as.matrix(dat_test[, -6007])
if(run.test_gbm){
load(file="../output/fit_train_gbm.RData")
tm_test <- system.time({prob_pred <- test_gbm(fit_train, feature_test, par_n.trees_best, pred.type = 'response');})
}
## reweight the test data to represent a balanced label distribution
label_test <- as.integer(dat_test$label)
# DELETE
save(feature_test, file="../output/feature_test_tmp_only.RData")
save(label_test, file="../output/label_test_tmp_only.RData")
# DELETE
weight_test <- rep(NA, length(label_test))
for (v in unique(label_test)){
weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}
label_pred <- ifelse(prob_pred > 0.5, 1, 0)
label_test <- ifelse(label_test == 2, 1, 0)
accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)
tpr.fpr <- WeightedROC(prob_pred, label_test, weight_test)
auc <- WeightedAUC(tpr.fpr)
cat("The accuracy of model:", "GBM with n.trees =" , par_n.trees_best, "and shrinkage =", par_shrinkage_best, "is", accu*100, "%.\n")
The accuracy of model: GBM with n.trees = 500 and shrinkage = 0.05 is 71.24604 %.
cat("The AUC of model:", "GBM with n.trees =" , par_n.trees_best, "and shrinkage =", par_shrinkage_best, "is", auc, ".\n")
The AUC of model: GBM with n.trees = 500 and shrinkage = 0.05 is 0.7879471 .
Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited.
cat("Time for constructing training features=", tm_feature_train[3], "s \n")
cat("Time for constructing testing features=", tm_feature_test[3], "s \n")
cat("Time for training model=", tm_train[3], "s \n")
cat("Time for testing model=", tm_test[3], "s \n")
Call the train model and test model from library.
train.R and test.R should be wrappers for all your model training steps and your classification/prediction steps.
train.R
test.R
source("../lib/train_xgboost.R")
source("../lib/test_xgboost.R")
source("../lib/cross_validation_xgboost.R")
feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}
if(run.cv_xgboost){
res_cv_xgboost <- matrix(0, nrow = length(eta) * length(colsample_bytree), ncol = 6)
count = 0
for(i in 1:length(eta)){
for(j in 1:length(colsample_bytree)){
count = count + 1
cat("eta =", eta[i], "\n")
cat("colsample_bytree =", colsample_bytree[j], "\n")
res_cv <- cv.function_xgboost(feature_train, label_train, K
, eta[i], colsample_bytree[j], reweight = sample.reweight)
res_cv_xgboost[count,] <- c(eta[i], colsample_bytree[j], res_cv[1], res_cv[2], res_cv[3], res_cv[4])
}
}
colnames(res_cv_xgboost) <- c("eta","colsample_bytree","mean_error", "sd_error", "mean_AUC", "sd_AUC")
save(res_cv_xgboost, file="../output/res_cv_xgboost.RData")
}else{
load("../output/res_cv_xgboost.RData")
}
Visualize cross-validation results.
res_cv_xgboost <- as.data.frame(res_cv_xgboost)
if(run.cv_xgboost){
p1 <- res_cv_xgboost %>%
ggplot(aes(x = eta, y = mean_error,
ymin = mean_error - sd_error, ymax = mean_error + sd_error)) +
geom_crossbar() +
facet_wrap(~colsample_bytree)+
theme(axis.text.x = element_text(angle = 90, hjust = 1))
p2 <- res_cv_xgboost %>%
ggplot(aes(x = eta, y = mean_AUC,
ymin = mean_AUC - sd_AUC, ymax = mean_AUC + sd_AUC)) +
geom_crossbar() +
facet_wrap(~colsample_bytree)+
theme(axis.text.x = element_text(angle = 90, hjust = 1))
print(p1)
print(p2)
}
# par_n.trees_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 1])
# par_shrinkage_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 2])
# par_eta_best <- 0.1
# par_colsample_bytree_best <- 0.6
# par_eta_best <- 0.05
# par_colsample_bytree_best <- 0.2
# eta <- c(0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5)
# colsample_bytree <- c(0.2, 0.5, 0.6, 0.7, 0.8, 0.9)
par_eta_best <- 0.3
par_colsample_bytree_best <- 0.7
source("../lib/train_xgboost.R")
source("../lib/test_xgboost.R")
# training weights
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}
tm_train <- NA
if (sample.reweight){
tm_train <- system.time(fit_train <- train_xgboost(feature_train, label_train, par_eta_best, par_colsample_bytree_best, sample.reweight))
} else {
tm_train <- system.time(fit_train <- train_xgboost(feature_train, label_train, par_eta_best, par_colsample_bytree_best, NULL))
}
xgb.save(fit_train, "../output/fit_train_xgboost.model")
tm_test = NA
feature_test <- as.matrix(dat_test[, -6007])
if(run.test_xgboost){
fit_train <- xgb.load("../output/fit_train_xgboost.model")
tm_test <- system.time({prob_pred <- test_xgboost(fit_train, feature_test, pred.type = 'probability');})
}
## reweight the test data to represent a balanced label distribution
label_test <- as.integer(dat_test$label)
weight_test <- rep(NA, length(label_test))
for (v in unique(label_test)){
weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}
label_pred <- ifelse(prob_pred > 0.5, 1, 0)
label_test <- ifelse(label_test == 2, 1, 0)
accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)
tpr.fpr <- WeightedROC(prob_pred, label_test, weight_test)
auc <- WeightedAUC(tpr.fpr)
cat("The accuracy of model:", "XGBoost with eta =" , par_eta_best, "and colsample_bytree =", par_colsample_bytree_best, "is", accu*100, "%.\n")
cat("The AUC of model:", "XGBoost with eta =" , par_eta_best, "and colsample_bytree =", par_colsample_bytree_best, "is", auc, ".\n")
Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited.
cat("Time for constructing training features=", tm_feature_train[3], "s \n")
cat("Time for constructing testing features=", tm_feature_test[3], "s \n")
cat("Time for training model=", tm_train[3], "s \n")
cat("Time for testing model=", tm_test[3], "s \n")
Call the train model and test model from library.
train.R and test.R should be wrappers for all your model training steps and your classification/prediction steps.
train.R
test.R
source("../lib/train_dnn.R")
source("../lib/test_dnn.R")
source("../lib/cross_validation_dnn.R")
feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)
# if(run.cv_dnn){
# res_cv_dnn <- matrix(0, 1, ncol = 4)
#
# res_cv <- cv.function_dnn(features = feature_train, labels = label_train, K,
# reweight = sample.reweight)
#
# res_cv_dnn[1,] <- c(res_cv[1], res_cv[2], res_cv[3], res_cv[4])
#
# colnames(res_cv_dnn) <- c("mean_error", "sd_error", "mean_AUC", "sd_AUC")
# save(res_cv_dnn, file="../output/res_cv_dnn.RData")
# }else{
# load("../output/res_cv_dnn.RData")
# }
Visualize cross-validation results.
# res_cv_dnn <- as.data.frame(res_cv_dnn)
#
# if(run.cv_dnn){
# p1 <- res_cv_dnn %>%
# ggplot(aes(x = "DNN", y = mean_error,
# ymin = mean_error - sd_error, ymax = mean_error + sd_error)) +
# geom_crossbar() +
# theme(axis.text.x = element_text())
#
# p2 <- res_cv_dnn %>%
# ggplot(aes(x = "DNN", y = mean_AUC,
# ymin = mean_AUC - sd_AUC, ymax = mean_AUC + sd_AUC)) +
# geom_crossbar() +
# theme(axis.text.x = element_text())
#
# print(p1)
# print(p2)
# }
# par_n.trees_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 1])
# par_shrinkage_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 2])
source("../lib/train_dnn.R")
source("../lib/test_dnn.R")
feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)
feature_test <- as.matrix(dat_test[, -6007])
label_test <- as.integer(dat_test$label)
label_test <- ifelse(label_test == 2, 1, 0)
weight_test <- rep(NA, length(label_test))
# training weights
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}
for (v in unique(label_test)){
weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}
# validation_split <- 1 / 5
# validation_split <- 0
tm_train <- NA
if (sample.reweight){
tm_train <- system.time(fit_train <- train_dnn(feature_train, label_train, w = weight_train, feature_test, label_test, weight_test))
} else {
tm_train <- system.time(fit_train <- train_dnn(feature_train, label_train, w = NULL, feature_test, label_test, weight_test = NULL))
}
Epoch 1/100
1/48 [..............................] - ETA: 0s - loss: 1.9691 - accuracy: 0.3200
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48/48 [==============================] - 2s 33ms/step - loss: 1.8296 - accuracy: 0.4650
48/48 [==============================] - 2s 50ms/step - loss: 1.8296 - accuracy: 0.4650 - val_loss: 1.7043 - val_accuracy: 0.6833
Epoch 2/100
1/48 [..............................] - ETA: 0s - loss: 1.6416 - accuracy: 0.5200
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48/48 [==============================] - 2s 33ms/step - loss: 1.7345 - accuracy: 0.5583
48/48 [==============================] - 2s 38ms/step - loss: 1.7345 - accuracy: 0.5583 - val_loss: 1.6564 - val_accuracy: 0.6717
Epoch 3/100
1/48 [..............................] - ETA: 0s - loss: 1.6902 - accuracy: 0.6800
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48/48 [==============================] - 2s 33ms/step - loss: 1.6834 - accuracy: 0.6121
48/48 [==============================] - 2s 38ms/step - loss: 1.6834 - accuracy: 0.6121 - val_loss: 1.6197 - val_accuracy: 0.6633
Epoch 4/100
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48/48 [==============================] - 2s 33ms/step - loss: 1.6469 - accuracy: 0.6346
48/48 [==============================] - 2s 39ms/step - loss: 1.6469 - accuracy: 0.6346 - val_loss: 1.5948 - val_accuracy: 0.7217
Epoch 5/100
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48/48 [==============================] - 2s 33ms/step - loss: 1.6242 - accuracy: 0.6675
48/48 [==============================] - 2s 39ms/step - loss: 1.6242 - accuracy: 0.6675 - val_loss: 1.5798 - val_accuracy: 0.6933
Epoch 6/100
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48/48 [==============================] - 2s 33ms/step - loss: 1.6055 - accuracy: 0.6746
48/48 [==============================] - 2s 39ms/step - loss: 1.6055 - accuracy: 0.6746 - val_loss: 1.5461 - val_accuracy: 0.7050
Epoch 7/100
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48/48 [==============================] - 2s 36ms/step - loss: 1.5660 - accuracy: 0.6975
48/48 [==============================] - 2s 42ms/step - loss: 1.5660 - accuracy: 0.6975 - val_loss: 1.5521 - val_accuracy: 0.7467
Epoch 8/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.5425 - accuracy: 0.7167
48/48 [==============================] - 2s 44ms/step - loss: 1.5425 - accuracy: 0.7167 - val_loss: 1.5368 - val_accuracy: 0.7167
Epoch 9/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.5252 - accuracy: 0.7092
48/48 [==============================] - 2s 44ms/step - loss: 1.5252 - accuracy: 0.7092 - val_loss: 1.5197 - val_accuracy: 0.7317
Epoch 10/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.4983 - accuracy: 0.7225
48/48 [==============================] - 2s 44ms/step - loss: 1.4983 - accuracy: 0.7225 - val_loss: 1.4933 - val_accuracy: 0.7217
Epoch 11/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.4897 - accuracy: 0.7421
48/48 [==============================] - 2s 44ms/step - loss: 1.4897 - accuracy: 0.7421 - val_loss: 1.4799 - val_accuracy: 0.7717
Epoch 12/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.4862 - accuracy: 0.7304
48/48 [==============================] - 2s 44ms/step - loss: 1.4862 - accuracy: 0.7304 - val_loss: 1.5134 - val_accuracy: 0.7050
Epoch 13/100
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48/48 [==============================] - 2s 37ms/step - loss: 1.4485 - accuracy: 0.7433
48/48 [==============================] - 2s 44ms/step - loss: 1.4485 - accuracy: 0.7433 - val_loss: 1.4495 - val_accuracy: 0.7483
Epoch 14/100
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48/48 [==============================] - 2s 37ms/step - loss: 1.4501 - accuracy: 0.7375
48/48 [==============================] - 2s 44ms/step - loss: 1.4501 - accuracy: 0.7375 - val_loss: 1.4278 - val_accuracy: 0.7567
Epoch 15/100
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48/48 [==============================] - 2s 37ms/step - loss: 1.4129 - accuracy: 0.7513
48/48 [==============================] - 2s 43ms/step - loss: 1.4129 - accuracy: 0.7513 - val_loss: 1.4304 - val_accuracy: 0.7400
Epoch 16/100
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48/48 [==============================] - 2s 37ms/step - loss: 1.4200 - accuracy: 0.7496
48/48 [==============================] - 2s 43ms/step - loss: 1.4200 - accuracy: 0.7496 - val_loss: 1.4270 - val_accuracy: 0.7733
Epoch 17/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.4068 - accuracy: 0.7546
48/48 [==============================] - 2s 44ms/step - loss: 1.4068 - accuracy: 0.7546 - val_loss: 1.4173 - val_accuracy: 0.7550
Epoch 18/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.3848 - accuracy: 0.7667
48/48 [==============================] - 2s 44ms/step - loss: 1.3848 - accuracy: 0.7667 - val_loss: 1.4076 - val_accuracy: 0.7017
Epoch 19/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.3527 - accuracy: 0.7658
48/48 [==============================] - 2s 44ms/step - loss: 1.3527 - accuracy: 0.7658 - val_loss: 1.3857 - val_accuracy: 0.7500
Epoch 20/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.3480 - accuracy: 0.7783
48/48 [==============================] - 2s 44ms/step - loss: 1.3480 - accuracy: 0.7783 - val_loss: 1.4350 - val_accuracy: 0.7283
Epoch 21/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.3240 - accuracy: 0.7783
48/48 [==============================] - 2s 44ms/step - loss: 1.3240 - accuracy: 0.7783 - val_loss: 1.3714 - val_accuracy: 0.7817
Epoch 22/100
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48/48 [==============================] - 2s 37ms/step - loss: 1.3000 - accuracy: 0.7954
48/48 [==============================] - 2s 44ms/step - loss: 1.3000 - accuracy: 0.7954 - val_loss: 1.3989 - val_accuracy: 0.7550
Epoch 23/100
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48/48 [==============================] - 2s 37ms/step - loss: 1.2966 - accuracy: 0.7962
48/48 [==============================] - 2s 43ms/step - loss: 1.2966 - accuracy: 0.7962 - val_loss: 1.3428 - val_accuracy: 0.8083
Epoch 24/100
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48/48 [==============================] - 2s 37ms/step - loss: 1.2804 - accuracy: 0.7850
48/48 [==============================] - 2s 44ms/step - loss: 1.2804 - accuracy: 0.7850 - val_loss: 1.3808 - val_accuracy: 0.7717
Epoch 25/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.2746 - accuracy: 0.7971
48/48 [==============================] - 2s 44ms/step - loss: 1.2746 - accuracy: 0.7971 - val_loss: 1.3547 - val_accuracy: 0.7783
Epoch 26/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.2312 - accuracy: 0.8117
48/48 [==============================] - 2s 45ms/step - loss: 1.2312 - accuracy: 0.8117 - val_loss: 1.3480 - val_accuracy: 0.7950
Epoch 27/100
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48/48 [==============================] - 2s 37ms/step - loss: 1.2284 - accuracy: 0.8108
48/48 [==============================] - 2s 43ms/step - loss: 1.2284 - accuracy: 0.8108 - val_loss: 1.3284 - val_accuracy: 0.7983
Epoch 28/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.2067 - accuracy: 0.8158
48/48 [==============================] - 2s 44ms/step - loss: 1.2067 - accuracy: 0.8158 - val_loss: 1.3604 - val_accuracy: 0.8050
Epoch 29/100
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48/48 [==============================] - 2s 37ms/step - loss: 1.2155 - accuracy: 0.8104
48/48 [==============================] - 2s 43ms/step - loss: 1.2155 - accuracy: 0.8104 - val_loss: 1.3412 - val_accuracy: 0.8217
Epoch 30/100
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48/48 [==============================] - 2s 37ms/step - loss: 1.1832 - accuracy: 0.8167
48/48 [==============================] - 2s 43ms/step - loss: 1.1832 - accuracy: 0.8167 - val_loss: 1.3361 - val_accuracy: 0.8150
Epoch 31/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.1767 - accuracy: 0.8067
48/48 [==============================] - 2s 44ms/step - loss: 1.1767 - accuracy: 0.8067 - val_loss: 1.3106 - val_accuracy: 0.8000
Epoch 32/100
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48/48 [==============================] - 2s 37ms/step - loss: 1.1643 - accuracy: 0.8200
48/48 [==============================] - 2s 44ms/step - loss: 1.1643 - accuracy: 0.8200 - val_loss: 1.3046 - val_accuracy: 0.7983
Epoch 33/100
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48/48 [==============================] - 2s 37ms/step - loss: 1.1572 - accuracy: 0.8204
48/48 [==============================] - 2s 44ms/step - loss: 1.1572 - accuracy: 0.8204 - val_loss: 1.2972 - val_accuracy: 0.7983
Epoch 34/100
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48/48 [==============================] - 2s 37ms/step - loss: 1.1138 - accuracy: 0.8288
48/48 [==============================] - 2s 44ms/step - loss: 1.1138 - accuracy: 0.8288 - val_loss: 1.2904 - val_accuracy: 0.8100
Epoch 35/100
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48/48 [==============================] - 2s 37ms/step - loss: 1.1309 - accuracy: 0.8283
48/48 [==============================] - 2s 43ms/step - loss: 1.1309 - accuracy: 0.8283 - val_loss: 1.2425 - val_accuracy: 0.8150
Epoch 36/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.1121 - accuracy: 0.8242
48/48 [==============================] - 2s 45ms/step - loss: 1.1121 - accuracy: 0.8242 - val_loss: 1.2423 - val_accuracy: 0.8100
Epoch 37/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.0655 - accuracy: 0.8375
48/48 [==============================] - 2s 44ms/step - loss: 1.0655 - accuracy: 0.8375 - val_loss: 1.2992 - val_accuracy: 0.8183
Epoch 38/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.0859 - accuracy: 0.8271
48/48 [==============================] - 2s 44ms/step - loss: 1.0859 - accuracy: 0.8271 - val_loss: 1.2781 - val_accuracy: 0.7817
Epoch 39/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.0498 - accuracy: 0.8425
48/48 [==============================] - 2s 44ms/step - loss: 1.0498 - accuracy: 0.8425 - val_loss: 1.2702 - val_accuracy: 0.8200
Epoch 40/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.0366 - accuracy: 0.8504
48/48 [==============================] - 2s 44ms/step - loss: 1.0366 - accuracy: 0.8504 - val_loss: 1.2544 - val_accuracy: 0.7950
Epoch 41/100
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48/48 [==============================] - 2s 38ms/step - loss: 1.0356 - accuracy: 0.8404
48/48 [==============================] - 2s 45ms/step - loss: 1.0356 - accuracy: 0.8404 - val_loss: 1.2352 - val_accuracy: 0.7983
Epoch 42/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.9884 - accuracy: 0.8683
48/48 [==============================] - 2s 44ms/step - loss: 0.9884 - accuracy: 0.8683 - val_loss: 1.2488 - val_accuracy: 0.8167
Epoch 43/100
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48/48 [==============================] - 2s 37ms/step - loss: 0.9803 - accuracy: 0.8579
48/48 [==============================] - 2s 44ms/step - loss: 0.9803 - accuracy: 0.8579 - val_loss: 1.2973 - val_accuracy: 0.8033
Epoch 44/100
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48/48 [==============================] - 2s 37ms/step - loss: 1.0014 - accuracy: 0.8587
48/48 [==============================] - 2s 43ms/step - loss: 1.0014 - accuracy: 0.8587 - val_loss: 1.2702 - val_accuracy: 0.7767
Epoch 45/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.9960 - accuracy: 0.8650
48/48 [==============================] - 2s 44ms/step - loss: 0.9960 - accuracy: 0.8650 - val_loss: 1.2273 - val_accuracy: 0.7917
Epoch 46/100
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48/48 [==============================] - 2s 37ms/step - loss: 0.9688 - accuracy: 0.8537
48/48 [==============================] - 2s 44ms/step - loss: 0.9688 - accuracy: 0.8537 - val_loss: 1.2365 - val_accuracy: 0.7967
Epoch 47/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.9365 - accuracy: 0.8737
48/48 [==============================] - 2s 44ms/step - loss: 0.9365 - accuracy: 0.8737 - val_loss: 1.2343 - val_accuracy: 0.8100
Epoch 48/100
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48/48 [==============================] - 2s 37ms/step - loss: 0.9241 - accuracy: 0.8683
48/48 [==============================] - 2s 44ms/step - loss: 0.9241 - accuracy: 0.8683 - val_loss: 1.3262 - val_accuracy: 0.8283
Epoch 49/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.9705 - accuracy: 0.8529
48/48 [==============================] - 2s 44ms/step - loss: 0.9705 - accuracy: 0.8529 - val_loss: 1.1856 - val_accuracy: 0.7867
Epoch 50/100
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48/48 [==============================] - 2s 37ms/step - loss: 0.9225 - accuracy: 0.8533
48/48 [==============================] - 2s 44ms/step - loss: 0.9225 - accuracy: 0.8533 - val_loss: 1.2344 - val_accuracy: 0.7867
Epoch 51/100
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48/48 [==============================] - 2s 37ms/step - loss: 0.9180 - accuracy: 0.8562
48/48 [==============================] - 2s 43ms/step - loss: 0.9180 - accuracy: 0.8562 - val_loss: 1.1941 - val_accuracy: 0.8183
Epoch 52/100
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48/48 [==============================] - 2s 37ms/step - loss: 0.8954 - accuracy: 0.8683
48/48 [==============================] - 2s 44ms/step - loss: 0.8954 - accuracy: 0.8683 - val_loss: 1.1645 - val_accuracy: 0.8000
Epoch 53/100
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48/48 [==============================] - 2s 37ms/step - loss: 0.9187 - accuracy: 0.8537
48/48 [==============================] - 2s 43ms/step - loss: 0.9187 - accuracy: 0.8537 - val_loss: 1.2104 - val_accuracy: 0.7617
Epoch 54/100
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48/48 [==============================] - 2s 37ms/step - loss: 0.8770 - accuracy: 0.8637
48/48 [==============================] - 2s 44ms/step - loss: 0.8770 - accuracy: 0.8637 - val_loss: 1.1738 - val_accuracy: 0.7800
Epoch 55/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.8635 - accuracy: 0.8708
48/48 [==============================] - 2s 44ms/step - loss: 0.8635 - accuracy: 0.8708 - val_loss: 1.0848 - val_accuracy: 0.7967
Epoch 56/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.8447 - accuracy: 0.8779
48/48 [==============================] - 2s 44ms/step - loss: 0.8447 - accuracy: 0.8779 - val_loss: 1.1465 - val_accuracy: 0.7983
Epoch 57/100
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48/48 [==============================] - 2s 37ms/step - loss: 0.8338 - accuracy: 0.8763
48/48 [==============================] - 2s 43ms/step - loss: 0.8338 - accuracy: 0.8763 - val_loss: 1.1932 - val_accuracy: 0.8383
Epoch 58/100
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48/48 [==============================] - 2s 37ms/step - loss: 0.8207 - accuracy: 0.8942
48/48 [==============================] - 2s 43ms/step - loss: 0.8207 - accuracy: 0.8942 - val_loss: 1.1169 - val_accuracy: 0.8150
Epoch 59/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.8082 - accuracy: 0.8808
48/48 [==============================] - 2s 45ms/step - loss: 0.8082 - accuracy: 0.8808 - val_loss: 1.1488 - val_accuracy: 0.8367
Epoch 60/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.8195 - accuracy: 0.8829
48/48 [==============================] - 2s 44ms/step - loss: 0.8195 - accuracy: 0.8829 - val_loss: 1.1227 - val_accuracy: 0.8017
Epoch 61/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.8023 - accuracy: 0.8854
48/48 [==============================] - 2s 44ms/step - loss: 0.8023 - accuracy: 0.8854 - val_loss: 1.1743 - val_accuracy: 0.8183
Epoch 62/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.7722 - accuracy: 0.8892
48/48 [==============================] - 2s 44ms/step - loss: 0.7722 - accuracy: 0.8892 - val_loss: 1.1386 - val_accuracy: 0.8367
Epoch 63/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.7802 - accuracy: 0.8917
48/48 [==============================] - 2s 44ms/step - loss: 0.7802 - accuracy: 0.8917 - val_loss: 1.2621 - val_accuracy: 0.8150
Epoch 64/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.7872 - accuracy: 0.8804
48/48 [==============================] - 2s 44ms/step - loss: 0.7872 - accuracy: 0.8804 - val_loss: 1.1195 - val_accuracy: 0.8183
Epoch 65/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.7587 - accuracy: 0.8946
48/48 [==============================] - 2s 44ms/step - loss: 0.7587 - accuracy: 0.8946 - val_loss: 1.1353 - val_accuracy: 0.7800
Epoch 66/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.7677 - accuracy: 0.8804
48/48 [==============================] - 2s 44ms/step - loss: 0.7677 - accuracy: 0.8804 - val_loss: 1.1352 - val_accuracy: 0.8267
Epoch 67/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.7324 - accuracy: 0.9021
48/48 [==============================] - 2s 45ms/step - loss: 0.7324 - accuracy: 0.9021 - val_loss: 1.1710 - val_accuracy: 0.7983
Epoch 68/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.7221 - accuracy: 0.8954
48/48 [==============================] - 2s 44ms/step - loss: 0.7221 - accuracy: 0.8954 - val_loss: 1.1456 - val_accuracy: 0.8000
Epoch 69/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.7235 - accuracy: 0.8850
48/48 [==============================] - 2s 45ms/step - loss: 0.7235 - accuracy: 0.8850 - val_loss: 1.2059 - val_accuracy: 0.8167
Epoch 70/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.7166 - accuracy: 0.8963
48/48 [==============================] - 2s 44ms/step - loss: 0.7166 - accuracy: 0.8963 - val_loss: 1.1929 - val_accuracy: 0.7817
Epoch 71/100
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48/48 [==============================] - 2s 36ms/step - loss: 0.7152 - accuracy: 0.9029
48/48 [==============================] - 2s 43ms/step - loss: 0.7152 - accuracy: 0.9029 - val_loss: 1.1496 - val_accuracy: 0.7983
Epoch 72/100
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48/48 [==============================] - 2s 37ms/step - loss: 0.7179 - accuracy: 0.8971
48/48 [==============================] - 2s 44ms/step - loss: 0.7179 - accuracy: 0.8971 - val_loss: 1.1589 - val_accuracy: 0.7800
Epoch 73/100
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48/48 [==============================] - 2s 39ms/step - loss: 0.6974 - accuracy: 0.8971
48/48 [==============================] - 2s 45ms/step - loss: 0.6974 - accuracy: 0.8971 - val_loss: 1.1537 - val_accuracy: 0.8183
Epoch 74/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6962 - accuracy: 0.8896
48/48 [==============================] - 2s 45ms/step - loss: 0.6962 - accuracy: 0.8896 - val_loss: 1.1282 - val_accuracy: 0.8300
Epoch 75/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6954 - accuracy: 0.8971
48/48 [==============================] - 2s 45ms/step - loss: 0.6954 - accuracy: 0.8971 - val_loss: 1.1093 - val_accuracy: 0.7917
Epoch 76/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6766 - accuracy: 0.9083
48/48 [==============================] - 2s 44ms/step - loss: 0.6766 - accuracy: 0.9083 - val_loss: 1.1003 - val_accuracy: 0.8083
Epoch 77/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6721 - accuracy: 0.9054
48/48 [==============================] - 2s 44ms/step - loss: 0.6721 - accuracy: 0.9054 - val_loss: 1.1293 - val_accuracy: 0.8017
Epoch 78/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6562 - accuracy: 0.9158
48/48 [==============================] - 2s 44ms/step - loss: 0.6562 - accuracy: 0.9158 - val_loss: 1.1924 - val_accuracy: 0.8100
Epoch 79/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6596 - accuracy: 0.8983
48/48 [==============================] - 2s 45ms/step - loss: 0.6596 - accuracy: 0.8983 - val_loss: 1.2203 - val_accuracy: 0.8433
Epoch 80/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6821 - accuracy: 0.8975
48/48 [==============================] - 2s 44ms/step - loss: 0.6821 - accuracy: 0.8975 - val_loss: 1.0704 - val_accuracy: 0.8233
Epoch 81/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6657 - accuracy: 0.9087
48/48 [==============================] - 2s 44ms/step - loss: 0.6657 - accuracy: 0.9087 - val_loss: 1.1067 - val_accuracy: 0.7817
Epoch 82/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6661 - accuracy: 0.8946
48/48 [==============================] - 2s 45ms/step - loss: 0.6661 - accuracy: 0.8946 - val_loss: 1.2199 - val_accuracy: 0.7950
Epoch 83/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6413 - accuracy: 0.9025
48/48 [==============================] - 2s 45ms/step - loss: 0.6413 - accuracy: 0.9025 - val_loss: 1.1322 - val_accuracy: 0.7933
Epoch 84/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6400 - accuracy: 0.9133
48/48 [==============================] - 2s 45ms/step - loss: 0.6400 - accuracy: 0.9133 - val_loss: 1.1503 - val_accuracy: 0.8150
Epoch 85/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6232 - accuracy: 0.9150
48/48 [==============================] - 2s 44ms/step - loss: 0.6232 - accuracy: 0.9150 - val_loss: 1.1511 - val_accuracy: 0.8183
Epoch 86/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6490 - accuracy: 0.9013
48/48 [==============================] - 2s 44ms/step - loss: 0.6490 - accuracy: 0.9013 - val_loss: 1.1314 - val_accuracy: 0.8000
Epoch 87/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6318 - accuracy: 0.9104
48/48 [==============================] - 2s 44ms/step - loss: 0.6318 - accuracy: 0.9104 - val_loss: 1.0846 - val_accuracy: 0.7867
Epoch 88/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6379 - accuracy: 0.8975
48/48 [==============================] - 2s 44ms/step - loss: 0.6379 - accuracy: 0.8975 - val_loss: 1.1112 - val_accuracy: 0.7983
Epoch 89/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6087 - accuracy: 0.9133
48/48 [==============================] - 2s 44ms/step - loss: 0.6087 - accuracy: 0.9133 - val_loss: 1.1846 - val_accuracy: 0.8050
Epoch 90/100
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48/48 [==============================] - 2s 37ms/step - loss: 0.6001 - accuracy: 0.9179
48/48 [==============================] - 2s 44ms/step - loss: 0.6001 - accuracy: 0.9179 - val_loss: 1.1631 - val_accuracy: 0.8233
Epoch 91/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.5999 - accuracy: 0.9117
48/48 [==============================] - 2s 44ms/step - loss: 0.5999 - accuracy: 0.9117 - val_loss: 1.1360 - val_accuracy: 0.8217
Epoch 92/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6043 - accuracy: 0.9179
48/48 [==============================] - 2s 44ms/step - loss: 0.6043 - accuracy: 0.9179 - val_loss: 1.1409 - val_accuracy: 0.8267
Epoch 93/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.5810 - accuracy: 0.9221
48/48 [==============================] - 2s 45ms/step - loss: 0.5810 - accuracy: 0.9221 - val_loss: 1.0561 - val_accuracy: 0.8333
Epoch 94/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.5980 - accuracy: 0.9196
48/48 [==============================] - 2s 45ms/step - loss: 0.5980 - accuracy: 0.9196 - val_loss: 1.0835 - val_accuracy: 0.8233
Epoch 95/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.6143 - accuracy: 0.9096
48/48 [==============================] - 2s 44ms/step - loss: 0.6143 - accuracy: 0.9096 - val_loss: 1.0680 - val_accuracy: 0.7917
Epoch 96/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.5947 - accuracy: 0.9175
48/48 [==============================] - 2s 45ms/step - loss: 0.5947 - accuracy: 0.9175 - val_loss: 1.0798 - val_accuracy: 0.8150
Epoch 97/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.5747 - accuracy: 0.9237
48/48 [==============================] - 2s 45ms/step - loss: 0.5747 - accuracy: 0.9237 - val_loss: 1.1478 - val_accuracy: 0.7883
Epoch 98/100
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48/48 [==============================] - 2s 39ms/step - loss: 0.5813 - accuracy: 0.9250
48/48 [==============================] - 2s 45ms/step - loss: 0.5813 - accuracy: 0.9250 - val_loss: 1.0969 - val_accuracy: 0.7967
Epoch 99/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.5952 - accuracy: 0.9246
48/48 [==============================] - 2s 45ms/step - loss: 0.5952 - accuracy: 0.9246 - val_loss: 1.1217 - val_accuracy: 0.7800
Epoch 100/100
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48/48 [==============================] - 2s 38ms/step - loss: 0.5826 - accuracy: 0.9096
48/48 [==============================] - 2s 45ms/step - loss: 0.5826 - accuracy: 0.9096 - val_loss: 1.1541 - val_accuracy: 0.8083
save_model_hdf5(fit_train, "../output/fit_train_dnn.h5")
tm_test = NA
feature_test <- as.matrix(dat_test[, -6007])
if(run.test_dnn){
fit_train <- load_model_hdf5("../output/fit_train_dnn.h5")
tm_test <- system.time({label_pred <- test_dnn(fit_train, feature_test, type = "predict_classes");
prob_pred <- test_dnn(fit_train, feature_test, type = "predict_proba")})
}
## reweight the test data to represent a balanced label distribution
label_test <- as.integer(dat_test$label)
weight_test <- rep(NA, length(label_test))
for (v in unique(label_test)){
weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}
label_test <- ifelse(label_test == 2, 1, 0)
accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)
# prob_pred <- apply(prob_pred, 1, max)
prob_pred <- prob_pred[, 2]
tpr.fpr <- WeightedROC(prob_pred, label_test, weight_test)
auc <- WeightedAUC(tpr.fpr)
cat("The accuracy of model:", "DNN", "is", accu*100, "%.\n")
The accuracy of model: DNN is 76.99475 %.
cat("The AUC of model:", "DNN", "is", auc, ".\n")
The AUC of model: DNN is 0.8527232 .
Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited.
cat("Time for constructing training features=", tm_feature_train[3], "s \n")
cat("Time for constructing testing features=", tm_feature_test[3], "s \n")
cat("Time for training model=", tm_train[3], "s \n")
cat("Time for testing model=", tm_test[3], "s \n")
###Reference - Du, S., Tao, Y., & Martinez, A. M. (2014). Compound facial expressions of emotion. Proceedings of the National Academy of Sciences, 111(15), E1454-E1462.